Abstract: Predicting the development height of the hydraulic fracture zone is vital to safe coal mining. This study first analyzed the development patterns of hydraulic fracture zones under similar mining conditions. Taking 36 sets of data measuring the development height of hydraulic fracture zones under similar geological conditions in Shandong mining area as example for analysis, we selected coal seam thickness, mining depth, sloping length of working face, and hard rock lithology ratio coefficient as the main control factors for the prediction model. We analyzed their correlation with the development height of hydraulic fracture zones, and established a multifactorial prediction model with highly-correlated factors via regression analysis and deep learning calculation. Compare and analyze the specification value of the prediction model with the measured data and the "triple down" specification data. Results show that compared with the measured value of hydraulic fracture zones, only 6 % and 17 % of the "triple down" specification data exhibit less than 5m of absolute value of the prediction error, while those in the 2 prediction models via regression analysis and deep learning are 83 % and 89 % respectively. The two prediction models show high curve fit, and their stability and accuracy outperform that of the "triple down" model.